Bubble Volume Measurement Method Development
Procedure
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h1. Secondary Experiments h2. Procedure {float:right|border=1px2px solid black}[!SandFilterSetup2.png|hspace=5,width=600px!|First Sand Filter Setup with Bubble Collector] h5. First version of the experimental setup that included a bubble collector {float} |
Since
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there
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were
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problems
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with
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the
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DO
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probes
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we
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used
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in
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the
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initial
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experimental
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setup,
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we
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switched
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to
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measuring
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the
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total
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volume
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of
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the
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bubbles
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that
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form
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inside
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the
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filter
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column.
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Using
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MathCad
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,
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we
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used
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the
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bubble
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volume
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collected
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to
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calculate
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the
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equivalent
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DO
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concentration
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removed
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from
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the
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water.
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The
...
only
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changes
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in
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the
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setup
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were
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to
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remove
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the
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DO
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probes
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and
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instead
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feed
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the
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water
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leaving
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the
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filter
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column
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into
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an
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inverted
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graduated
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cylinder.
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This
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cylinder
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was
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filled
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with water at the start of each run and had its mouth submerged in water. As bubbles formed in the filter media, they flowed out into this cylinder and floated to the top, displacing some water and causing the water level inside the cylinder to fall. The air volume was measured every 10 minutes during each run, using the calibrations on the side of the cylinder.
We also tried a new method of testing the quality of the effluent water, using sugar. The sugar test involved collecting the outflowing water in a clear beaker or cylinder and adding some sugar. As the sugar dissolved in the water, if tiny gas bubbles were seen floating to the surface, the water is still super-saturated with gas and our filter method did not work. If no bubbles were seen, then the water was no longer super-saturated, and it could be assumed that the gases were removed.
Results
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water and had its mouth submerged in water. As bubbles formed in the filter media, they flowed out into this cylinder and floated to the top, displacing some water and causing the water level inside the cylinder to fall. The air volume was measured every 10 minutes during each run, using the calibrations on the side of the cylinder. \\ h2. Results {float:right|border=1px2px solid black|width=500px} |{anchor:graph of bubble volume vs. timeFigure 1} !BubbleVolFig.1, vol vs. Timetime, 32 200mLmincm.png|width=500px,align=center! h5. Figure 1: Total bubblegas volume removed collected overfrom water vs. time with a flow rate of \\200 ml/min and a sand depth of 32 cm.| |{anchor:results for varied bed depth} !BubbleVol, varied depth.png|width=500px! h5. The effect of varying glass bead depth at a flow rate of \\200 ml/min.| {float} The first experiment run using this bubble collection method used glass beads as the filter media, with a flow rate of 200 ml/min and an unsuspended filter depth of 32 cm. We observed that the performance of the system increased for about 20 minutes, after which the rate of the increase in air volume became relatively constant. These results are illustrated in the [#graph of bubble volume vs. time]. After 20 minutes, the line becomes nearly linear. We concluded that the experiment needs run for at least 20 minutes in order for the data to become steady and reliable, and that we would start recording data after at least 20 minutes of runtime. Next, we experimented with the glass bead depth. At a flow rate of 200 mL/min, we obtained the following results: The [#results for varied bed depth] show that a glass bead depth of 10cm would work better than the larger glass bead depth of 32cm. Logically, this did not make sense, so we repeated the experiment at a later date. Figures 3 and 4 below illustrate these results, which show that a larger filter depth extracted more DO. \\ {float:right|border=1px solid black}!DO removed vs. time, fig 3.png|width= by glass beads. Flow rate: 200 mL/min. Bed depth: 32 cm. {float} |
The first experiment run using this bubble collection method used glass beads as the filter media, with a flow rate of 200 ml/min and an unsuspended filter depth of 32 cm. We observed that the performance of the system increased for about 20 minutes, after which the rate of the increase in air volume became relatively constant. These results are illustrated in #Figure 1. After 20 minutes, the line of gas volume vs. time becomes nearly linear. We concluded that the experiment needs run for at least 20 minutes in order for the data to become steady and reliable, and that we would start recording data after at least 20 minutes of runtime.
Since the graph for gas volume vs. time is basically linear, we could accurately fit a linear trendline to the data using Excel, as shown in #Figure 1. The slope of this trendline represents the rate that the gas is being removed from the water, in mL/min. This rate can be converted to equivalent mL of gas removed per liter of water running through the filter column by dividing the slope of the line by the flow rate in L/min. #Table 1 summarizes this process for the first run with glass beads.
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h5. Table 1: Gas removed by glass beads. Depth: 32 cm
||Flow Rate (mL/min)||Slope (mL/min)||Gas Removed (mL/L water)||
|200|.7727|3.86|
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!Fig.2, vol vs. time, 2 depths.png|width=500px,align=center!
h5. Figure 2: Total gas volume removed from water vs. time by glass beads. Flow rate: 200 mL/min. Bed depths: 32 and 10 cm
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Next, we experimented with the glass bead depth. obtaining the results shown in #Figure 2. The data in #Figure 2 was treated the same way as for the first trial (#Figure 1), and #Table 2 shows the resulting gas removal for each bed depth in mL of gas per L of water. As can be seen in the table, at a flow rate of 200 mL/min, the filter depth of 10 cm appeared to remove more gas than the larger glass bead depth of 32 cm.
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h5. Table 2: Gas removed by glass beads at varied depths
||Bed Depth (cm)||Flow Rate (mL/min)||Slope (mL/min)||Gas Removed (mL/L water)||
|10|200|4.9255|24.63|
|32|200|.78|3.9|
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This did not make sense, so we repeated the experiment later. #Figures 3 and 4 illustrate these results, showing that a larger filter depth did in fact extract a greater volume of dissolved gas at a faster rate.
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{float:left|border=2px solid black|width=1100px} |{anchor:Figure 5} !Fig. 3, vol vs. time, depths and flow rates.png|width=500px,align=center! h5. Figure 3: Gas volume vs.500px! *Figure 3* DO concentration versus time at varying depths and \\flow rates.|{float} {float:left|border=1px solid black}!DO removedanchor:Figure 6} !Fig. 4, vol vs. time, fig 4depths adn flow rates 2.png|width=500px,align=center! *h5. Figure 4*: DOGas concentrationvolume versusvs. time at varying depths and flow \\rates. This graph is the same as Figure 3, but the results from theExcludes results of flow \\rate of= 150 ml/min atand depth = 33 cm.| {float} |
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h5. Table 3: Gas removed by glass beads at varied depths and flow rates
||Bed Depth (cm)||Flow Rate (mL/min)||Slope (mL/min)||Gas Removed (mL/L water)||
|33|200|.6268|3.14|
|10|200|.429|2.15|
|10|150|.3149|1.43|
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As shown in #Figure 3, the volume of gas removed from the water flowing at 150 mL/min through the 33 cm filter was much greater than that removed at the other flow rates and depths. This was probably because the 33 cm at 150 mL/min run was after the 10 cm runs, which meant we needed to add glass beads to the column. A lot of air came in with the dry beads, and we most likely did not allow sufficient run time afterward in order to allow all the trapped air to escape before we began recording the data. This resulted in the extremely high volumes of gas apparently being removed by our system. By the time we ran that depth at the lower flow rate, the extra air had left and we were gathering only the air being stripped from the water by our filter column.
#Figure 4 shows the same results as #Figure 3 but omits the erroneous 150 mL/min at 33 cm data. Clearly, the greater filter depth resulted in the removal of more dissolved gas than the lower depth. The flow rate was not varied enough to have much impact on the effectiveness of the filter method. Trendlines were once again fitted to the gas volume vs. time curves in #Figure 4, and the resulting gas removals are shown in #Table 3.
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{float:right|border=2px solid black|width=500px} {anchor:Figure 5} !Fig.5, DO red. rate vs. time, no outlier filter depth are excluded.{float} \\ The DO stripped from the water flowing at 150 mL/min through the 33 cm filter was much greater than at the other flow rates and depths. This was probably because the 150mL/min run was at a greater filter depth, which meant we needed to add glass beads to the column. A lot of air came in with the dry beads, and we most likely did not allow sufficient runtime afterward in order to allow all the trapped air to escape before we began recording the data. By the time we ran that depth at the lower flow rate, the extra air had left and we were gathering only the air being stripped from the water. Figure 4, above, shows the same results as Figure 3 but omits the 150 mL/min at 33 cm data, making the results from this run easier to see. Clearly, the greater filter depth resulted in the removal of more dissolved oxygen than the lower depth. The flow rate was not varied enough to have much impact on the effectiveness of this method. We compared the DO removal according to the DO probes and to our calculations using the total collected bubble volume. {float:left|border=1px solid black}!DO removed, measured and calculated, 10cm.png|width=500px! *h5. Figure 5*: DO removed,reduction measuredrate byvs.time theat DOvarying probesdepths asand well as by \\calculating equivalent DO using the collected bubble volume. Sand \\depth of 10 cm, flow rate of 200 mL/min.{float} {float:left|border=1px solid black}flow rates. {float} |
We also used our Mathcad program to convert the volume of gas we were collecting to an equivalent concentration of dissolved oxygen being removed from the water. #Figure 5 shows the results of this for the three runs shown in #Figure 4. These values for DO removal could not be taken as accurate, however, because the Mathcad program assumes that oxygen is the only gas super-saturating the water. In reality, other gases such as nitrogen could be present. Though the values are not exact, their relative positions on the graph may be used to draw some conclusions.
#Figure 5 shows that the rate of dissolved oxygen removal increased for a short amount of time at the start of each run, but then tended to level off into a steady removal rate as time went on. The higher rate of DO reduction exhibited by the run of the deeper bed depth (33cm) shows again that a deeper bed is more effective.
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{float:right|border=2px solid black|width=500px} {anchor:Figure 6} !DO removed, measured and calculated, 33cm.png|width=500px! *h5. Figure 6*: DO concentration removed, measured by the DO probes \\as well as by calculating equivalent DO using the collected bubble \\volume. Sand depth of 3310 cm, flow rate of 200 mL/min. {float} \\ Figures 5 and 6 further show that the DO concentrations recorded by the DO probes were not reliable. The DO stripped from the water according to the DO probes appeared very different from the DO removed that was calculated from the collected air volume. Figure 5 reveals a large discrepancy between the DO probes' data and the actual volume of water collected. Figure 6 shows that the DO probes were in fact recording that oxygen was _added_ to the water during the process, although the volume of air collected from the water was definitely increasing. h2. Conclusion We have not yet drawn any final conclusions. The data collected was not reliable or stable, due to many problems, such as the DO probes not reading the concentration correctly. We may tentatively conclude that greater filter depth removes more dissolved oxygen, but further research is necessary. We will be able to draw firmer conclusions when the problems of the setup are fixed and we have a more accurate and precise method of measuring the collected air volume. After this round of experiments, we altered our setup to include a new bubble collector and a chamber to make sure the water is super-saturated when entering our filter. [See the method and results here.|Current Experiments] |
#Figure 6 compares the DO removal as measured by the DO probes to our calculations using the total collected bubble volume for the 200 mL/min run at 10 cm depth. The graph reveals a large discrepancy between the DO probes' data and the numbers derived from the actual volume of water collected. Again, although the calculated DO-removal values cannot be trusted for accuracy, the overall trend of the numbers can be used for comparison. The negative DO reduction measured by the DO probes would mean that oxygen was added to the water inside the filter column, although the increasing volume of air collected from the water showed that the opposite was true. We concluded from this that our DO probes were definitely not reliable.
The sugar test was performed after several of the runs, but the results varied greatly, even when performed on multiple effluent samples for the same run, or on tap water. Overall, the sugar test results were inconclusive.
Download data for these experiments here
Conclusion
From these results, we may tentatively conclude that greater filter depth removes more dissolved oxygen. However, the method of measuring and recording the volume of gas removed from the water was not precise, and it allowed for a large amount of error and inconsistency. We also cannot be sure that water entering the system was in fact supersaturated, as the temperature outside was growing warmer and varying greatly by the day. As such, we will be able to draw firmer conclusions when the setup includes a method of supersaturating the water and a more accurate and precise method of measuring the collected air volume.
The sugar test proved to be very inconsistent, but it may perform better if a finer sugar is used. This method requires further testing.
After this round of experiments, we altered our setup to include a new bubble collector and a chamber to make sure the water is super-saturated when entering our filter. See the method and results here.